CaptainCook4D: A dataset for understanding errors in procedural activities
CoRR(2023)
摘要
Following step-by-step procedures is an essential component of various
activities carried out by individuals in their daily lives. These procedures
serve as a guiding framework that helps to achieve goals efficiently, whether
it is assembling furniture or preparing a recipe. However, the complexity and
duration of procedural activities inherently increase the likelihood of making
errors. Understanding such procedural activities from a sequence of frames is a
challenging task that demands an accurate interpretation of visual information
and the ability to reason about the structure of the activity. To this end, we
collect a new egocentric 4D dataset, CaptainCook4D, comprising 384 recordings
(94.5 hours) of people performing recipes in real kitchen environments. This
dataset consists of two distinct types of activity: one in which participants
adhere to the provided recipe instructions and another in which they deviate
and induce errors. We provide 5.3K step annotations and 10K fine-grained action
annotations and benchmark the dataset for the following tasks: supervised error
recognition, multistep localization, and procedure learning
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